A database of a business entity, such as a financial institution, keeps track of many different attributes for a given financial transaction. Such attributes for a customer's transaction may include, for example, a parent company and its subsidiary, an amount of the transaction, a date of the transaction, the geographical location of the customer when the transaction occurred, the given time of the year when the transaction occurred, and many other attributes. It has been found that most of such attributes are interrelated, or correlated in mathematical terms.
A current problem faced by such a business entity is that it is very difficult to have all the attributes well defined for all the transactions. For example, some of the attributes will be missing, in which case the business entity must struggle to determine what are the critical attributes that are needed in order to monitor for any given set of transactions or any given set of problems. There is a present need for a way to address and solve this problem.
Further, most databases of business entities, such as financial institutions, currently have either missing, invalid or inaccurate data stored in them. Currently, when the business entity attempts to create data quality rules to assess data quality in its database, the number of data quality rules that are needed may run to many thousands of such rules. There is likewise a present need for a way to enable business entities to assess the data quality for a given database in terms of the validity of the data, the completeness of the data and/or the accuracy of the stored data.